In this work, we address these fundamental questions using a weakly supervised approach different from semi supervised learning: Multiple Instance Learning (MIL) [1]}, [2]}, [3]}. In MIL problems, one regards every input as a bag of instances, and the label of the bag is determined by the labels of its instances. This framework naturally fits the problem of hemorrhage detection in head CT, since an examination is considered positive (i.e., its coarse, global label is positive) as soon as it contains at least one image with evidence of hemorrhage (i.e., it contains an image with a positive local label). MIL is a particular case of weakly supervised learning, wherein labels are only available for bags (i.e., examinations) instead of instances (i.e., images). By employing a state-of-the-art MIL model [4]} that can be trained with either global or local labels, we study whether strong supervision with expensive local labels leads to significantly higher performance in hemorrhage detection in head CT, or whether weak supervision—which is cheaper to obtain—can provide comparable models.
